• Event: Learning Technologies UK 25
• Date: 24 April 2025
• By: Dr Daniel J Hulme
• Chair: Donald H Taylor
• Estimated read time: 7 minutes

 

---

 

Quick read summary

This session explored how artificial intelligence is reshaping the role of learning and development, not as a content factory, but as a core enabler of performance and productivity.

It matters now because AI is already being adopted across organisations to reduce cost, increase output and redesign operating models. Learning teams that remain focused on courses and platforms risk being sidelined as these decisions accelerate.

Readers will gain a clear view of how L&D can reposition itself around enablement, how AI changes the economics and architecture of learning, and what practical shifts leaders need to make to stay relevant.

 

---

 

From learning to enablement

The central argument of the session was simple but challenging. The traditional learning model no longer moves at the speed of the business.

L&D has historically operated as a publishing function. Needs are analysed, content is designed, reviewed, built, launched and then measured. That model worked when change was slower and knowledge was relatively stable. It struggles when roles, processes and expectations shift continuously.

AI exposes that mismatch. It makes it possible to deliver answers, guidance and support at the moment of need, without forcing people through full programmes or static curricula. This pushes L&D away from education as the primary outcome and towards enablement.

Enablement does not remove learning. It reframes it. Education becomes one of several tools used to help people perform, alongside decision support, coaching, feedback and access to organisational knowledge.

 

The superworker effect and why L&D is central

AI was positioned not as a replacement for people, but as a force multiplier. The session described a “superworker” effect, where individuals use AI to perform higher value work in different ways.

This shift is already visible across organisations. Executives are committing to AI driven productivity gains. Investment decisions are being made quickly. Learning is not being consulted first.

That creates both risk and opportunity for L&D. Risk, because learning teams can be bypassed. Opportunity, because learning sits at the intersection of content, skills, performance and behaviour, exactly where AI delivers the most value.

L&D functions already manage complex ecosystems of knowledge, tools and experiences. That makes them well placed to shape how AI is trained, governed and applied to real work, if they step into that role deliberately.

 

Why the current L&D operating model is under pressure

The session was direct in its assessment of the current state of L&D.

Budgets have not kept pace with inflation. Learning leadership has often been pushed down the organisational hierarchy. Responsibility for capability has fragmented across business units. Meanwhile, the core technology stack remains rooted in systems designed decades ago for tracking, not for intelligence.

Learning management systems still play an important role in compliance and reporting. However, they do not understand content at a meaningful level. They cannot adapt, infer intent or personalise without extensive manual effort.

AI changes this equation. When the platform can generate, interpret and connect content dynamically, the distinction between system and content begins to collapse. This is why AI based learning architectures feel fundamentally different rather than incremental.

 

The four stages of AI adoption in L&D

The session outlined a progression that many organisations will recognise.

The first stage is AI as an assistant. Individuals use tools to work faster, but the underlying system remains unchanged.

The second stage automates parts of the learning workflow. AI begins to generate content, structure programmes and handle tasks that previously required specialist effort.

The third stage connects systems together. Learning, performance, onboarding and experience platforms begin to share intelligence, enabling much larger productivity gains.

The fourth stage closes the loop. AI analyses real performance, identifies gaps and feeds targeted support back to individuals in near real time.

At this point, the question is no longer how to build courses, but how to design interventions, validate quality and ensure the right behaviours are being reinforced.

 

Enablement architecture, not more platforms

A recurring theme was that AI does not remove complexity. It shifts where the work sits.

Organisations will still have multiple sources of knowledge, from expert insight to process documentation. The challenge becomes deciding what is current, what is trusted and how it should be surfaced.

Learning teams have a critical role here. Not as owners of every tool, but as stewards of the enablement strategy. This includes decisions about governance, content quality, integration with enterprise AI assistants and collaboration with IT.

The session made it clear that learning cannot remain isolated from wider AI strategy. Enablement must be designed into the infrastructure of the organisation, not bolted on afterwards.

 

Practical application: what leaders should do next

Questions leaders should be asking

• Where does our business lose performance today due to lack of timely knowledge or support?
• Which roles would benefit most from learning in the flow of work rather than formal programmes?
• How is AI already being used by employees, with or without L&D involvement?

Signals to watch in the organisation

• AI investment decisions being made without reference to learning or capability.
• Line leaders building their own AI based support tools outside central governance.
• Growing pressure to demonstrate learning impact through operational outcomes rather than satisfaction scores.

Common pitfalls

• Treating AI as a faster way to produce the same courses.
• Waiting for perfect data or complete strategies before experimenting.
• Assuming technology alone will solve performance problems.

What good looks like in practice

• Learning teams working with IT and the business on shared AI priorities.
• Clear use cases tied to performance improvement, not content volume.
• L&D professionals spending more time on diagnosis, design and governance, and less on manual production.

 

Key takeaways

• AI accelerates the shift from learning delivery to performance enablement.
• The superworker model increases the strategic relevance of L&D, but only if teams step into it.
• Traditional learning architectures struggle to support real time, adaptive support.
• Enablement requires close alignment with enterprise AI and IT strategy.
• The biggest risk for L&D is not change, but waiting too long to engage with it.

 

Final thoughts

This session did not argue for incremental improvement. It described a structural shift in how organisations build capability.

AI is already reshaping work. Learning teams can either remain observers, or become designers of how people and technology perform together. The move from learning to enablement is not a loss of identity. It is an expansion of impact, if leaders are willing to take it.

 

Quote of the session

“We are really moving from the education business into the enablement business.”

Josh Bersin, Bersin & Associates

 

---

 

Speakers

Josh Bersin, Principal and founder, Bersin & Associates. Global industry analyst and researcher focused on HR, learning and workforce transformation.

Laura Overton, Analyst, Explorer, Writer, Facilitator, Learning Changemakers. Independent analyst and facilitator specialising in learning, capability and evidence based practice.

 

---